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People and companies shaping AI. Filter your watched accounts, catch up on recent posts, or find the most liked and viewed.

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209 watched accounts · 2870 matching posts · Showing up to 3,000 recent stored posts. Counts are snapshots, not live X totals. This page refreshes every minute while visible. Last collected .

  1. Every 📧

    @every ·

    Early this year we built Plus One, our personal-agent product. In May, we deleted 86,730 lines of its code because it wasn’t working.

    A personal bot like OpenClaw was too much work to run: Each agent lived on its own cloud computer, which was too high-maintenance. Many of us ended up abandoning our bots.

    For the bots that remained alive, we noticed that they were most useful when borrowed by other people. @tedescau connected his Plus One to our analytics tool, and people on the team asked it analytics questions.

    We decided to start over, making two changes: 1. We let Anthropic’s Claude Managed Agents take care of the infrastructure. 2. We replaced our fleet of personal agents with one shared company agent.

    @bigwilliestyle breaks down the process behind the creation of the Every Agent: https://every.to/source-code/why-we-replaced-personal-agents-with-one-for-the-whole-company?utm_source=x&utm_campaign=every-agent-launch&utm_content=every-shared-agent-starting-over

    Install the Every Agent: https://every.to/agent?utm_source=x&utm_campaign=every-agent-launch&utm_content=every-shared-agent-starting-over-install

  2. Dan Shipper

    @danshipper ·

    great to chat with the legend @nathanbarry !

  3. corbin

    @corbin_braun ·

    once you find the concept that you fully align with internally

    you work toward it with a level of focus

    that can not be acheived anywhere else.

    concept of

    I don't have enough energy is not even part of the equation

    it is quite the opposite.

    you have to do it,

    it is by nature.

  5. Artificial Analysis

    @ArtificialAnlys ·

    Phonic's Phonic 1 (High) is the new leading Speech to Speech model, debuting at #1 on the Artificial Analysis Speech to Speech Index at 87.1, while also leading on agentic performance and being competitive on latency

    Phonic 1 is @phonic_co 's Speech to Speech model, available through the Phonic API at $0.07 per minute. We evaluated Phonic 1 at its high intelligence level through the Phonic API.

    Key takeaways:

    ➤ Speech to Speech Index: #1 of 22 at 87.1, ahead of Grok Voice 3.0 (86.9) by a very small margin. The Index averages Speech Reasoning (Big Bench Audio), Agentic Performance (Tau Voice), Arena Preference and Task Success Rate

    ➤ Speech Agent Arena: #8 of 30 on preference (Elo 1012) and #4 on Task Success Rate (92.3%)

    ➤ Tau Voice: #1 at 79.9%, a clear lead of 5.4 points over the next model

    ➤ Big Bench Audio: #3 at 98.9%, behind StepAudio 3 Realtime (99.7%) and Qwen Audio 3.0 Realtime Plus (99.2%)

    ➤ Speed: #4 on Time to First Audio at 0.61s, and the fastest of the top 5 models in the Index

    ➤ Cost: $4.20 per hour of input audio

    Congrats to the @phonic_co team 🚀

    See below for more detail ⬇️

  6. elvis

    @omarsar0 ·

    We need to rethink agent infrastructure.

    Companies are quickly moving from chatbots to autonomous agents that do real work across their systems. The infrastructure under those agents hasn't caught up.

    @mintive is building that layer, focusing on governance, efficiency, and security. Agents get their own identities and permissions, traces are logged properly, and teams get token budgets to set clear limits.

  7. jack friks

    @jackfriks ·

    thank goodness i have post bridge MCP to schedule all these posts out using opus 5.5

    Media shared by jack friks
  8. Every 📧

    @every ·

    Mike as a service. Coming soon.

  9. dax

    @thdxr ·

    is now a good time to get into cybersecurity insurance

  10. Gabriel Chua

    @gabrielchua ·

    i like when my dot manages my codex cloud threads throughout the night

    Media shared by Gabriel Chua
  12. Ben Burtenshaw

    @ben_burtenshaw ·

    It was super fun to learn how @MistralAI built le chonk from the ground up in Europe in their own datasets. Why it excels at cyber. And why we should start building with it.

  13. Allie K. Miller

    @alliekmiller ·

    I’ve been texting with my OpenAI Dot for awhile and prefer it over the app.

    Favorite use case is just screenshotting something, texting it to Dottie, and give instructions.

    “Add to cal” “Handle this” “Ask Austin if he wants to go with me” “Add to my bucket list” “Explain this” “When can I do this?” “What will this conflict with?” “Remember I have to do this”

    Also recommend asking it to make a contact photo. See below for Dottie the Dalmatian.

    Media shared by Allie K. Miller
  14. Alex Imas

    @alexolegimas ·

    This is such an incredibly creative project.

  15. Every 📧

    @every ·

    Very little is free in NYC. But if you're a paid Every subscriber, our afterparty for Thesis: 2027 costs exactly $0.

    Your agent can work late. You have a party to go to. DJ. Passed bites. Open bar. And more.

    🥳 Join us at Pioneer Works in Red Hook, Brooklyn.

    🪩 November 5, 2026. 6–9 p.m. ET. 21+.

    🎟️ RSVP: https://every.to/thesis-2027/after-party/purchase?utm_source=x&utm_campaign=bau&utm_content=every-thesis-after-party-agent-work-late

    Media shared by Every 📧
  17. Dean W. Ball

    @deanwball ·

    I occasionally run into fortune 100 firms that have not adopting coding agents *at all.* adoption is still very very early.

  18. Dean W. Ball

    @deanwball ·

    "Yeah, people talk a lot about RSI as AI that does AI research. There's very little talk about self-replicating data centers, this idea that you can use AI to control robots and run fabs and build turbines or solar panels and build data centers. But I think we have that within grasp."

  19. Samuel Hammond 🦉

    @hamandcheese ·

    If any crash is coming for the stock market its for everything that's still radically mispricing strong AI. There may also be an extreme "end of ZIRP"-like tide-going-out effect on forms of hidden leverage that no longer make sense relative to the IRR of AI capex

  20. Andrew Curran

    @AndrewCurran_ ·

    I was going to post some new sama quotes from this interview this morning, but prinz already posted a great summary. I will add that this is the third time I've seen Mr Altman talk about self replicating OpenAI datacenters. The first use case for OpenAI robots appears to be building more OpenAI robots and building more OpenAI datacenters. Mr A also wrote specifically about this on his blog a couple of years ago.

    'Yeah, people talk a lot about RSI as AI that does AI research. There's very little talk about self-replicating data centers, this idea that you can use AI to control robots and run fabs and build turbines or solar panels and build data centers. But I think we have that within grasp.'

  22. Santiago

    @svpino ·

    This is going to be massive for companies:

    It's a service that sits between any AI software and the tools and model providers it uses.

    The idea is to let the company control who uses what from a central location, for example:

    • Control model access • Control model budgets • Control tool access • Control guardrails

  24. Riley Brown

    @rileybrown ·

    As of today October 29th. This is my current AI Stack.

    Mostly comprised of 2 agent tools, and 1 that allows me to centralize my plugins and skills which makes it easier to switch agent platforms.

    https://youtu.be/YT0u2eQ-Aj4?is=eRXb-ePvR8EnoGoo

  25. Samuel Hammond 🦉

    @hamandcheese ·

    This is with only ~50% adoption by the Fortune 500 and with very low spend. Diffusion is still incredibly early.

  26. Harrison Chase

    @hwchase17 ·

    browser use is one of the fastest growing agent primitives

    get started with it in managed deepagents today!

  27. Wes Bos

    @wesbos ·

    We had really funny names for the flavours but they were nixed by the organizers 😩

  28. Elon Musk

    @elonmusk ·

    Our civilization would be winning wondrously if we are exploring the galaxy with Von Neumann probes by 2100!

    Larry Page might be right. He has correctly predicted many things.

  30. Riley Goodside

    @goodside ·

    We’ve known since 2023 AI could not continue to improve due to “model collapse”—training on poisoned internet data generated by AI. This is why LLMs have made no progress since that time, steadily declining on all benchmarks, and are projected to disappear completely by 2030.